publications

publications by categories in reversed chronological order. generated by jekyll-scholar.

2025

  1. Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor
    Alexandra Olteanu, Su Lin Blodgett, Agathe Balayn, Angelina Wang, Fernando Diaz, Flavio du Pin Calmon, Margaret Mitchell, Michael Ekstrand, Reuben Binns, and Solon Barocas
    In Procedings of the Thirty-Ninth Annual Conference on Neural Information Processing Systems, Nov 2025
  2. What News Recommendation Research Did (But Mostly Didn’t) Teach Us About Building A News Recommender
    Karl Higley, Robin Burke, Michael D. Ekstrand, and Bart P. Knijnenburg
    In Proceedings of Beyond Algorithms: Reclaiming the Interdisciplinary Roots of Recommender Systems, Sep 2025
    DOI
  3. We’re Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later
    Alan Said, Maria Soledad Pera, and Michael D. Ekstrand
    In Proceedings of Beyond Algorithms: Reclaiming the Interdisciplinary Roots of Recommender Systems, Sep 2025
    DOI
  4. Recommending with, Not for: Co-Designing Recommender Systems for Social Good
    Michael D. Ekstrand, Afsaneh Razi, Aleksandra Sarcevic, Maria Soledad Pera, Robin Burke, and Katherine Landau Wright
    ACM Trans. Recomm. Syst., Aug 2025
    DOI
  5. Recall, Robustness, and Lexicographic Evaluation
    Fernando Diaz, Michael D. Ekstrand, and Bhaskar Mitra
    ACM Trans. Recomm. Syst., Jul 2025
    DOI
  6. The Impossibility of Fair LLMs
    Jacy Reese Anthis, Kristian Lum, Michael Ekstrand, Avi Feller, and Chenhao Tan
    In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, May 2025
    DOI
  7. User and Recommender Behavior over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation
    Samira Vaez Barenji, Sushobhan Parajuli, and Michael D. Ekstrand
    In Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, May 2025
    DOI
  8. The Evolving Landscape of Online Child Safety: Insights from Media Analysis
    Mohammad Namvarpour, Elham Aghakhani, Michael D. Ekstrand, Rezvaneh Rezapour, and Afsaneh Razi
    In Proceedings of the 17th ACM Web Science Conference, May 2025
    DOI

2024

  1. It’s Not You, It’s Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation
    Andres Ferraro, Michael D. Ekstrand, and Christine Bauer
    In Proceedings of the 18th ACM Conference on Recommender Systems, Oct 2024
    DOI
  2. Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
    Jonathan Stray, Alon Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Chloe Bakalar, Lex Beattie, Michael Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy Zhang, Mckane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, and Nina Vasan
    ACM Transactions on Recommender Systems, Jun 2024
    DOI
  3. The Impossibility of Fair LLMs
    Jacy Anthis, Kristian Lum, Michael Ekstrand, Avi Feller, Alexander D’Amour, and Chenhao Tan
    May 2024
    DOI
  4. Distributionally-Informed Recommender System Evaluation
    Michael D. Ekstrand, Ben Carterette, and Fernando Diaz
    ACM Transactions on Recommender Systems, Mar 2024
    DOI
  5. Not Just Algorithms: Strategically Addressing Consumer Impacts in Information Retrieval
    Michael D. Ekstrand, Lex Beattie, Maria Soledad Pera, and Henriette Cramer
    In Proceedings of the 46th European Conference on Information Retrieval, Mar 2024
    DOI
  6. Multiple Testing for IR and Recommendation System Experiments
    Ngozi Ihemelandu and Michael D. Ekstrand
    In Proceedings of the 46th European Conference on Information Retrieval, Mar 2024
    DOI
  7. Towards Optimizing Ranking in Grid-Layout for Provider-Side Fairness
    Amifa Raj and Michael D. Ekstrand
    In Proceedings of the 46th European Conference on Information Retrieval, Mar 2024
    DOI

2023

  1. Responsible AI Research Needs Impact Statements Too
    Alexandra Olteanu, Michael Ekstrand, Carlos Castillo, and Jina Suh
    Nov 2023
    DOI
  2. Candidate Set Sampling for Evaluating Top-N Recommendation
    Ngozi Ihemelandu and Michael D. Ekstrand
    In Proceedings of the 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, Oct 2023
    DOI